def testGapStatistic():
count = 10;
# X = np.array(np.loadtxt("data/kmeans1.txt", delimiter = "\t"));
# X = np.mat(np.load("/media/WindowsE/Data/PARS/JNLH/YuanLiaoDaiShui/trainSet/train_all_75.npy"))[:, 68];#57
# X = np.mat(np.load("/media/WindowsD/WorkSpace/data.npy"))[:, 0];
X = np.load("/media/WindowsE/Data/PARS/JNLH/ReasonAnalysis/30/2020-08-01/data.npy")[:, 1].reshape(-1, 1);
plt.figure(1, (12, 8));
plt.get_current_fig_manager().window.showMaximized();
plt.hist(X, bins = 1000);
plt.show(block=True);
selector1 = KMeans.GapStatistic(20, True);
selector2 = KMeans.ElbowMethod(True);
index, distance, center = KMeans.KMeans.optimalK(X, 5, count, KMeans.CombinationOptimalKSelector([selector1, selector2]));
# plt.figure(1, (12, 8));
# plt.get_current_fig_manager().window.showMaximized();
#
# colors = ["g", "b", "y", "k", "r", "m", "c"];
# markers = ["*", "+", "D", "s", "h", "v", "d"];
#
# for i in range(0, len(center)):
# plt.scatter(X[index == i, 0], X[index == i, 1], color = colors[i], marker = markers[i]);
# plt.scatter(center[i, 0], center[i, 1], color = ["r"], marker = "x");
#
# plt.show(block=True);
plt.figure(1, (12, 8));
plt.get_current_fig_manager().window.showMaximized();
plt.hist(X, bins=1000);
for i in range(len(center)):
plt.axvline(center[i, 0], color = "r");
plt.show(block=True);
def testElbowMethod():
count = 1;
# X = np.mat(np.loadtxt("data/kmeans1.txt", delimiter = "\t"));
# X = np.load("/media/WindowsE/Data/PARS/JNLH/YuanLiaoDaiShui/trainSet/train_all_75.npy")[:, 57].reshape(-1, 1);
X = np.load("/media/WindowsE/Data/PARS/JNLH/ReasonAnalysis/30/2020-08-01/data.npy")[:, 1].reshape(-1, 1);
plt.figure(1, (12, 8));
plt.get_current_fig_manager().window.showMaximized();
plt.hist(X, bins = 1000);
plt.show(block=True);
selector = KMeans.ElbowMethod(True);
index, distance, center = KMeans.KMeans.optimalK(X, 5, count, selector);
plt.figure(1, (12, 8));
plt.get_current_fig_manager().window.showMaximized();
plt.hist(X, bins=1000);
for i in range(len(center)):
plt.axvline(center[i, 0], color = "r");
plt.show(block=True);
print("the optimal k is {0}".format(len(center)));
print("the centers is:\r\n{0}".format(center));
